docs: def _mirror_penalized(self, f_values, idx):
          """obsolete and subject to removal (TODO),
          return modified f-values such that for each mirror one becomes worst.
  
          This function is useless when selective mirroring is applied with no
          more than (lambda-mu)/2 solutions.
  
          Mirrors are leading and trailing values in ``f_values``.
  
          """
          assert len(f_values) >= 2 * len(idx)
          m = np.max(np.abs(f_values))
          for i in len(idx):
              if f_values[idx[i]] > f_values[-1 - i]:
                  f_values[idx[i]] += m
              else:
                  f_values[-1 - i] += m
          return f_values